class_docstr: Beam search decoder for RNN-T model with biasing support.
  
  Args:
      model (RNNT): RNN-T model to use.
      blank (int): index of blank token in vocabulary.
      temperature (float, optional): temperature to apply to joint network output.
          Larger values yield more uniform samples. (Default: 1.0)
      hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
          for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
          hypothesis score normalized by token sequence length. (Default: None)
      step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
      trie (list, optional): the prefix tree for TCPGen biasing
      biasing (bool, optional): If true, do biasing, otherwise use standard RNN-T support
code: def _gen_a_hypos(
      self,
      a_hypos: List[Hypothesis],
      b_hypos: List[Hypothesis],
      next_token_probs: torch.Tensor,
      t: int,
      beam_width: int,
      device: torch.device,
  ) -> List[Hypothesis]:
      (
          nonblank_nbest_scores,
          nonblank_nbest_hypo_idx,
          nonblank_nbest_token,
      ) = _compute_updated_scores(a_hypos, next_token_probs, beam_width)
  
      if len(b_hypos) < beam_width:
          b_nbest_score = -float("inf")
      else:
          b_nbest_score = _get_hypo_score(b_hypos[-beam_width])
  
      base_hypos: List[Hypothesis] = []
      new_tokens: List[int] = []
      new_scores: List[float] = []
      for i in range(beam_width):
          score = float(nonblank_nbest_scores[i])
          if score > b_nbest_score:
              a_hypo_idx = int(nonblank_nbest_hypo_idx[i])
              base_hypos.append(a_hypos[a_hypo_idx])
              new_tokens.append(int(nonblank_nbest_token[i]))
              new_scores.append(score)
  
      if base_hypos:
          new_hypos = self._gen_new_hypos(
              base_hypos, new_tokens, new_scores, t, device
          )
      else:
          new_hypos: List[Hypothesis] = []
  
      return new_hypos
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
  from torchaudio.prototype.models.rnnt import TrieNode
label_desc: Use image node_id 7 for calling the RNNTBeamSearchBiasing obj's underlying member method code with example usage: obj._gen_a_hypos(a_hypos, b_hypos, next_token_probs, t, beam_width, device) and returns: new_hypos